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<div class="title">TensorConvolutionSycl.h</div>  </div>
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<div class="contents">
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">// This file is part of Eigen, a lightweight C++ template library</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// for linear algebra.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">// Mehdi Goli    Codeplay Software Ltd.</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment">// Ralph Potter  Codeplay Software Ltd.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment">// Luke Iwanski  Codeplay Software Ltd.</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment">// Contact: &lt;eigen@codeplay.com&gt;</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment">// Copyright (C) 2016 Benoit Steiner &lt;benoit.steiner.goog@gmail.com&gt;</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160; </div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment">// This Source Code Form is subject to the terms of the Mozilla</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment">// Public License v. 2.0. If a copy of the MPL was not distributed</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment">// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160; </div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H</span></div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="preprocessor">#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H</span></div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160; </div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &quot;./InternalHeaderCheck.h&quot;</span></div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160; </div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespaceEigen.html">Eigen</a> {</div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160; </div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="keyword">enum class</span> convolution_type { CONV1D, CONV2D, CONV3D };</div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> CoeffReturnType, <span class="keyword">typename</span> KernelType, <span class="keyword">typename</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, <span class="keyword">typename</span> InputDims,</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;          <span class="keyword">typename</span> Kernel_accessor, <span class="keyword">typename</span> Buffer_accessor, convolution_type Conv_Dim&gt;</div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="keyword">struct </span>EigenConvolutionKernel;</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> CoeffReturnType, <span class="keyword">typename</span> KernelType, <span class="keyword">typename</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, <span class="keyword">typename</span> InputDims,</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;          <span class="keyword">typename</span> Kernel_accessor, <span class="keyword">typename</span> Buffer_accessor&gt;</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="keyword">struct </span>EigenConvolutionKernel&lt;Evaluator, CoeffReturnType, KernelType, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, InputDims, Kernel_accessor,</div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;                              Buffer_accessor, convolution_type::CONV1D&gt; {</div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;  <span class="keyword">typedef</span> cl::sycl::accessor&lt;CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local&gt;</div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;      Local_accessor;</div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;  Local_accessor local_acc;</div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;  Evaluator device_evaluator;</div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;  Kernel_accessor kernel_filter;</div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;  Buffer_accessor buffer_acc;</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;  internal::IndexMapper&lt;Index, InputDims, 1, Evaluator::Layout&gt; indexMapper;</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize;</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;  <span class="keyword">const</span> cl::sycl::range&lt;2&gt; input_range;</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;                         Buffer_accessor buffer_acc_,</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;                         internal::IndexMapper&lt;Index, InputDims, 1, Evaluator::Layout&gt; indexMapper_,</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;                         <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize_, <span class="keyword">const</span> cl::sycl::range&lt;2&gt; input_range_)</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;      : local_acc(local_acc_),</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;        device_evaluator(device_evaluator_),</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;        kernel_filter(kernel_filter_),</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;        buffer_acc(buffer_acc_),</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;        indexMapper(indexMapper_),</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;        kernelSize(kernelSize_),</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;        input_range(input_range_) {}</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160; </div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> BooleanDim2&gt;</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">bool</span> boundary_check(<span class="keyword">const</span> BooleanDim2 boolean_check) {</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    <span class="keywordflow">return</span> (boolean_check[0] &amp;&amp; boolean_check[1]);</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  }</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  <span class="keywordtype">void</span> operator()(cl::sycl::nd_item&lt;2&gt; itemID) {</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <span class="keyword">auto</span> buffer_ptr = buffer_acc.get_pointer();</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    <span class="keyword">auto</span> kernel_ptr = kernel_filter.get_pointer();</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    <span class="comment">// the required row to be calculated for the for each plane in shered memory</span></div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_input = (itemID.get_local_range()[0] + kernelSize - 1);</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> plane_kernel_offset = itemID.get_local_id(1) * num_input;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> input_offset = itemID.get_group(0) * itemID.get_local_range()[0];</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> plane_tensor_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(1));</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = itemID.get_local_id(0); i &lt; num_input; i += itemID.get_local_range()[0]) {</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_index = i + plane_kernel_offset;</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> tensor_index =</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;          plane_tensor_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i + input_offset);</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160; </div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;      local_acc[local_index] =</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;          (((i + input_offset) &lt; (input_range[0] + kernelSize - 1)) &amp;&amp; itemID.get_global_id(1) &lt; input_range[1])</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;              ? device_evaluator.coeff(tensor_index)</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;              : CoeffReturnType(0);</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    }</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160; </div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    itemID.barrier(cl::sycl::access::fence_space::local_space);</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160; </div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    <span class="comment">// calculate the convolution // output start x</span></div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> first_output_start = itemID.get_group(0) * (itemID.get_local_range()[0]);</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <span class="keywordflow">if</span> (boundary_check(itemID.get_global_id() &lt; input_range)) {</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;      CoeffReturnType result = <span class="keyword">static_cast&lt;</span>CoeffReturnType<span class="keyword">&gt;</span>(0);</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> index = plane_kernel_offset + itemID.get_local_id(0);</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k &lt; kernelSize; ++k) {</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        result += (local_acc[k + index] * kernel_ptr[k]);</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;      }</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> tensor_index =</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(1)) +</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + first_output_start);</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;      buffer_ptr[tensor_index] = result;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    }</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  }</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;};</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160; </div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> CoeffReturnType, <span class="keyword">typename</span> KernelType, <span class="keyword">typename</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, <span class="keyword">typename</span> InputDims,</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;          <span class="keyword">typename</span> Kernel_accessor, <span class="keyword">typename</span> Buffer_accessor&gt;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;<span class="keyword">struct </span>EigenConvolutionKernel&lt;Evaluator, CoeffReturnType, KernelType, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, InputDims, Kernel_accessor,</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;                              Buffer_accessor, convolution_type::CONV2D&gt; {</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  <span class="keyword">typedef</span> cl::sycl::accessor&lt;CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local&gt;</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;      Local_accessor;</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;  Local_accessor local_acc;</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  Evaluator device_evaluator;</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  Kernel_accessor kernel_filter;</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;  Buffer_accessor buffer_acc;</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;  internal::IndexMapper&lt;Index, InputDims, 2, Evaluator::Layout&gt; indexMapper;</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  <span class="keyword">const</span> cl::sycl::range&lt;2&gt; kernel_size;</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;  <span class="keyword">const</span> cl::sycl::range&lt;3&gt; input_range;</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;                         Buffer_accessor buffer_acc_,</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;                         internal::IndexMapper&lt;Index, InputDims, 2, Evaluator::Layout&gt; indexMapper_,</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;                         <span class="keyword">const</span> cl::sycl::range&lt;2&gt; kernel_size_, <span class="keyword">const</span> cl::sycl::range&lt;3&gt; input_range_)</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;      : local_acc(local_acc_),</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;        device_evaluator(device_evaluator_),</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        kernel_filter(kernel_filter_),</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        buffer_acc(buffer_acc_),</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        indexMapper(indexMapper_),</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        kernel_size(kernel_size_),</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        input_range(input_range_) {}</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> BooleanDim3&gt;</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">bool</span> boundary_check(<span class="keyword">const</span> BooleanDim3 boolean_check) {</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordflow">return</span> (boolean_check[0] &amp;&amp; boolean_check[1] &amp;&amp; boolean_check[2]);</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  }</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160; </div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;  <span class="keywordtype">void</span> operator()(cl::sycl::nd_item&lt;3&gt; itemID) {</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="keyword">auto</span> buffer_ptr = buffer_acc.get_pointer();</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    <span class="keyword">auto</span> kernel_ptr = kernel_filter.get_pointer();</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    <span class="comment">// the required row to be calculated for the for each plane in shered memory</span></div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> num_input = cl::sycl::range&lt;2&gt;{</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;        (cl::sycl::range&lt;2&gt;(itemID.get_local_range()[0], itemID.get_local_range()[1]) + kernel_size - 1)};</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160; </div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(2));</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> plane_kernel_offset = itemID.get_local_id(2) * num_input[1];</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160; </div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> input_offset = cl::sycl::range&lt;2&gt;{itemID.get_group(0) * itemID.get_local_range()[0],</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;                                                 itemID.get_group(1) * itemID.get_local_range()[1]};</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;      </div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="comment">// fill the local memory</span></div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    <span class="keywordtype">bool</span> in_range_dim2 = itemID.get_global_id(2) &lt; input_range[2];</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = itemID.get_local_id(1); j &lt; num_input[1]; j += itemID.get_local_range()[1]) {</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_input_offset = num_input[0] * (j + plane_kernel_offset);</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      <span class="keywordtype">bool</span> in_range_dim1 = ((j + input_offset[1]) &lt; (input_range[1] + kernel_size[1] - 1)); </div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = itemID.get_local_id(0); i &lt; num_input[0]; i += itemID.get_local_range()[0]) {</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_index = i + local_input_offset;</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;                                                             i + input_offset[0], j + input_offset[1]);</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;        local_acc[local_index] = (((i + input_offset[0]) &lt; (input_range[0] + kernel_size[0] - 1)) &amp;&amp;</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;                                  in_range_dim1 &amp;&amp; in_range_dim2)</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;                                     ? device_evaluator.coeff(tensor_index)</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;                                     : CoeffReturnType(0);</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;      }</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    }</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160; </div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    itemID.barrier(cl::sycl::access::fence_space::local_space);</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    <span class="comment">// output offset start for each thread</span></div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> output_offset = cl::sycl::range&lt;2&gt;{itemID.get_group(0) * itemID.get_local_range()[0],</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;                                                  itemID.get_group(1) * itemID.get_local_range()[1]};</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160; </div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    <span class="keywordflow">if</span> (boundary_check(itemID.get_global_id() &lt; input_range)) {</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;      CoeffReturnType result = <span class="keyword">static_cast&lt;</span>CoeffReturnType<span class="keyword">&gt;</span>(0);</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160; </div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; kernel_size[1]; j++) {</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;        <span class="keywordtype">size_t</span> kernel_offset = kernel_size[0] * j;</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> index =</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;            (num_input[0] * (plane_kernel_offset + j + itemID.get_local_id(1))) + itemID.get_local_id(0);</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; kernel_size[0]; i++) {</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;          result += (local_acc[i + index] * kernel_ptr[i + kernel_offset]);</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;        }</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;      }</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> tensor_index =</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(2)) +</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + output_offset[0],</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;                                                             itemID.get_local_id(1) + output_offset[1]);</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;      buffer_ptr[tensor_index] = result;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    }</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;  }</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;};</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160; </div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> CoeffReturnType, <span class="keyword">typename</span> KernelType, <span class="keyword">typename</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, <span class="keyword">typename</span> InputDims,</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;          <span class="keyword">typename</span> Kernel_accessor, <span class="keyword">typename</span> Buffer_accessor&gt;</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;<span class="keyword">struct </span>EigenConvolutionKernel&lt;Evaluator, CoeffReturnType, KernelType, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, InputDims, Kernel_accessor,</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;                              Buffer_accessor, convolution_type::CONV3D&gt; {</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;  <span class="keyword">typedef</span> cl::sycl::accessor&lt;CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local&gt;</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;      Local_accessor;</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;  Local_accessor local_acc;</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;  Evaluator device_evaluator;</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;  Kernel_accessor kernel_filter;</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;  Buffer_accessor buffer_acc;</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  internal::IndexMapper&lt;Index, InputDims, 3, Evaluator::Layout&gt; indexMapper;</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  <span class="keyword">const</span> cl::sycl::range&lt;3&gt; kernel_size;</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="keyword">const</span> cl::sycl::range&lt;3&gt; input_range;</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> numP;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160; </div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;                         Buffer_accessor buffer_acc_,</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;                         internal::IndexMapper&lt;Index, InputDims, 3, Evaluator::Layout&gt; indexMapper_,</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;                         <span class="keyword">const</span> cl::sycl::range&lt;3&gt; kernel_size_, <span class="keyword">const</span> cl::sycl::range&lt;3&gt; input_range_,</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;                         <span class="keyword">const</span> <span class="keywordtype">size_t</span> numP_)</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;      : local_acc(local_acc_),</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        device_evaluator(device_evaluator_),</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        kernel_filter(kernel_filter_),</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        buffer_acc(buffer_acc_),</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        indexMapper(indexMapper_),</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;        kernel_size(kernel_size_),</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;        input_range(input_range_),</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        numP(numP_) {}</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> BooleanDim3&gt;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">bool</span> boundary_check(<span class="keyword">const</span> BooleanDim3 boolean_check) {</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <span class="keywordflow">return</span> (boolean_check[0] &amp;&amp; boolean_check[1] &amp;&amp; boolean_check[2]);</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;  <span class="keywordtype">void</span> operator()(cl::sycl::nd_item&lt;3&gt; itemID) {</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    <span class="keyword">auto</span> buffer_ptr = buffer_acc.get_pointer();</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    <span class="keyword">auto</span> kernel_ptr = kernel_filter.get_pointer();</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> num_input = cl::sycl::range&lt;3&gt;{itemID.get_local_range() + kernel_size - 1};</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160; </div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> input_offset = cl::sycl::range&lt;3&gt;{itemID.get_group().get_id() * itemID.get_local_range()};</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160; </div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> output_offset =</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;          cl::sycl::range&lt;3&gt;{itemID.get_group().get_id() * itemID.get_local_range() + itemID.get_local_id()};</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160; </div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p = 0; p &lt; numP; p++) {</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">size_t</span> plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = itemID.get_local_id(2); k &lt; num_input[2]; k += itemID.get_local_range()[2]) {</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        <span class="keywordtype">size_t</span> local_index_dim2 = num_input[0] * num_input[1] * k;</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        <span class="keywordtype">bool</span> cond_k_dim = (k + input_offset[2] &lt; (input_range[2] + kernel_size[2] - 1));</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = itemID.get_local_id(1); j &lt; num_input[1]; j += itemID.get_local_range()[1]) {</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;          <span class="keywordtype">bool</span> cond_j_dim = cond_k_dim &amp;&amp; (j + input_offset[1] &lt; (input_range[1] + kernel_size[1] - 1));</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;          <span class="keywordtype">size_t</span> local_index_dim1 = (num_input[0] * j)  + local_index_dim2;</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = itemID.get_local_id(0); i &lt; num_input[0]; i += itemID.get_local_range()[0]) {</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;            <span class="keywordtype">bool</span> conds = cond_j_dim &amp;&amp; (i + input_offset[0] &lt; (input_range[0] + kernel_size[0] - 1));</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_index = local_index_dim1 + i;</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">size_t</span> tensor_index =</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;                plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;                                         i + input_offset[0], j + input_offset[1], k + input_offset[2]);</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;            local_acc[local_index] = conds ? device_evaluator.coeff(tensor_index) : CoeffReturnType(0);</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;          }</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        }</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;      }</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;      itemID.barrier(cl::sycl::access::fence_space::local_space);</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160; </div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;      <span class="comment">// calculate the convolution</span></div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160; </div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;      <span class="keywordflow">if</span> (boundary_check(itemID.get_global_id() &lt; input_range)) {</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        CoeffReturnType result = <span class="keyword">static_cast&lt;</span>CoeffReturnType<span class="keyword">&gt;</span>(0);</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k &lt; kernel_size[2]; k++) {</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; kernel_size[1]; j++) {</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; kernel_size[0]; i++) {</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;              <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernel_index = i + kernel_size[0] * (j + kernel_size[1] * k);</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;              <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_index =</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;                  ((i + itemID.get_local_id(0)) +</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;                   num_input[0] * ((j + itemID.get_local_id(1)) + num_input[1] * (k + itemID.get_local_id(2))));</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160; </div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;              result += (local_acc[local_index] * kernel_ptr[kernel_index]);</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;            }</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;          }</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        }</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> tensor_index =</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;            indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p) +</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;            indexMapper.mapGpuOutputKernelToTensorOutputOffset(output_offset[0], output_offset[1], output_offset[2]);</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        buffer_ptr[tensor_index] = result;</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;      }</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160; </div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;      itemID.barrier(cl::sycl::access::fence_space::local_space);</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    }</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  }</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;};</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160; </div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Indices, <span class="keyword">typename</span> InputArgType, <span class="keyword">typename</span> KernelArgType&gt;</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;<span class="keyword">struct </span>TensorEvaluator&lt;const TensorConvolutionOp&lt;Indices, InputArgType, KernelArgType&gt;, <a class="code" href="namespaceEigen.html">Eigen</a>::SyclDevice&gt; {</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;  <span class="keyword">typedef</span> TensorConvolutionOp&lt;Indices, InputArgType, KernelArgType&gt; XprType;</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160; </div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDims =</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;      internal::array_size&lt;typename TensorEvaluator&lt;InputArgType, Eigen::SyclDevice&gt;::Dimensions&gt;::value;</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumKernelDims = internal::array_size&lt;Indices&gt;::value;</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprType::Index <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>;</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;Index, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> TensorEvaluator&lt;KernelArgType, Eigen::SyclDevice&gt;::Dimensions KernelDimensions;</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">const</span> Eigen::SyclDevice Device;</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprType::CoeffReturnType CoeffReturnType;</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> PacketType&lt;CoeffReturnType, Eigen::SyclDevice&gt;::type PacketReturnType;</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> InputArgType::Scalar Scalar;</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> PacketSize = PacketType&lt;CoeffReturnType, Device&gt;::size;</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;  <span class="keyword">typedef</span> StorageMemory&lt;CoeffReturnType, Eigen::SyclDevice&gt; Storage;</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Storage::Type EvaluatorPointerType;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;  <span class="keyword">typedef</span> StorageMemory&lt;const CoeffReturnType, Eigen::SyclDevice&gt; KernelStorage;</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160; </div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> Layout = TensorEvaluator&lt;InputArgType, Eigen::SyclDevice&gt;::Layout;</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    IsAligned = TensorEvaluator&lt;InputArgType, Eigen::SyclDevice&gt;::IsAligned &amp;</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;                TensorEvaluator&lt;KernelArgType, Eigen::SyclDevice&gt;::IsAligned,</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    PacketAccess = <span class="keyword">false</span>,</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    BlockAccess = <span class="keyword">false</span>,</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    PreferBlockAccess = <span class="keyword">false</span>,</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    CoordAccess = <span class="keyword">false</span>,  <span class="comment">// to be implemented</span></div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    RawAccess = <span class="keyword">false</span></div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;  };</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160; </div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;  <span class="comment">//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//</span></div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;  <span class="keyword">typedef</span> internal::TensorBlockNotImplemented TensorBlock;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;  <span class="comment">//===--------------------------------------------------------------------===//</span></div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160; </div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;  TensorEvaluator(<span class="keyword">const</span> XprType &amp;op, <span class="keyword">const</span> Eigen::SyclDevice &amp;device)</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;      : m_inputImpl(op.inputExpression(), device),</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;        m_kernelArg(op.kernelExpression()),</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;        m_kernelImpl(op.kernelExpression(), device),</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;        m_indices(op.indices()),</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;        m_buf(NULL),</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        m_kernel(NULL),</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;        m_local_kernel(false),</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;        m_device(device) {</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    EIGEN_STATIC_ASSERT((<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(TensorEvaluator&lt;InputArgType, Eigen::SyclDevice&gt;::Layout) ==</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;                         <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(TensorEvaluator&lt;KernelArgType, Eigen::SyclDevice&gt;::Layout)),</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;                        YOU_MADE_A_PROGRAMMING_MISTAKE);</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160; </div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <span class="keyword">const</span> <span class="keyword">typename</span> TensorEvaluator&lt;InputArgType, Eigen::SyclDevice&gt;::Dimensions &amp;input_dims = m_inputImpl.dimensions();</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="keyword">const</span> <span class="keyword">typename</span> TensorEvaluator&lt;KernelArgType, Eigen::SyclDevice&gt;::Dimensions &amp;kernel_dims =</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;        m_kernelImpl.dimensions();</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160; </div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    m_dimensions = m_inputImpl.dimensions();</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; NumKernelDims; ++i) {</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> index = op.indices()[i];</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> input_dim = input_dims[index];</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> kernel_dim = kernel_dims[i];</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> result_dim = input_dim - kernel_dim + 1;</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;      m_dimensions[index] = result_dim;</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    }</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;  }</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160; </div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">const</span> Dimensions &amp;dimensions()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_dimensions; }</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160; </div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;  EIGEN_STRONG_INLINE <span class="keywordtype">bool</span> evalSubExprsIfNeeded(EvaluatorPointerType data) {</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    preloadKernel();</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    m_inputImpl.evalSubExprsIfNeeded(NULL);</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    <span class="keywordflow">if</span> (data) {</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;      executeEval(data);</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;      <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;      m_buf = (EvaluatorPointerType)m_device.get(</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;          (Scalar *)m_device.allocate_temp(dimensions().TotalSize() * <span class="keyword">sizeof</span>(Scalar)));</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;      executeEval(m_buf);</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;      <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;    }</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;  }</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160; </div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;  EIGEN_STRONG_INLINE <span class="keywordtype">void</span> cleanup() {</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    m_inputImpl.cleanup();</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    <span class="keywordflow">if</span> (m_buf) {</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;      m_device.deallocate_temp(m_buf);</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;      m_buf = NULL;</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    }</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    <span class="keywordflow">if</span> (m_local_kernel) {</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;      m_device.deallocate_temp(m_kernel);</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;      m_local_kernel = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    }</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    m_kernel = NULL;</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;  }</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keyword">const</span> Device &amp;device()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_device; }</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_buf; }</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160; </div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">void</span> preloadKernel() {</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    <span class="comment">// Don&#39;t make a local copy of the kernel unless we have to (i.e. it&#39;s an</span></div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;    <span class="comment">// expression that needs to be evaluated)</span></div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="keyword">typename</span> KernelStorage::Type in_place = m_kernelImpl.data();</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="keywordflow">if</span> (in_place) {</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;      m_kernel = in_place;</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;      m_local_kernel = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;      ptrdiff_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * <span class="keyword">sizeof</span>(Scalar);</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;      EvaluatorPointerType local = (EvaluatorPointerType)m_device.get((Scalar *)m_device.allocate_temp(kernel_sz));</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;      <span class="keyword">typedef</span> TensorEvalToOp&lt;const KernelArgType&gt; EvalTo;</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;      EvalTo evalToTmp(m_device.get(local), m_kernelArg);</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">bool</span> PacketAccess = internal::IsVectorizable&lt;Eigen::SyclDevice, KernelArgType&gt;::value;</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;      internal::TensorExecutor&lt;const EvalTo, Eigen::SyclDevice, PacketAccess&gt;::run(evalToTmp, m_device);</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;      m_kernel = local;</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;      m_local_kernel = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    }</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;  }</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160; </div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">void</span> executeEval(EvaluatorPointerType data)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    <span class="keyword">typedef</span> TensorEvaluator&lt;InputArgType, Eigen::SyclDevice&gt; InputEvaluator;</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> InputEvaluator::Dimensions InputDims;</div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <span class="keywordflow">switch</span> (NumKernelDims) {</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;      <span class="keywordflow">case</span> 1: {</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numX = dimensions()[m_indices[0]];</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numP = dimensions().TotalSize() / numX;</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> input_dim = std::array&lt;size_t, 2&gt;{numX, numP};</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;        <span class="keyword">auto</span> global_range = cl::sycl::range&lt;2&gt;{};</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;        <span class="keyword">auto</span> local_range = cl::sycl::range&lt;2&gt;{};</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernel_size = m_kernelImpl.dimensions().TotalSize();</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160; </div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;        m_device.parallel_for_setup(input_dim, global_range, local_range);</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_memory_size = (local_range[0] + kernel_size - 1) * (local_range[1]);</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;        gpu_assert(<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">long</span><span class="keyword">&gt;</span>(local_memory_size) &lt;= m_device.sharedMemPerBlock());</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;        <span class="keyword">const</span> array&lt;Index, 1&gt; indices{{m_indices[0]}};</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;        <span class="keyword">const</span> array&lt;Index, 1&gt; kernel_dims{{m_kernelImpl.dimensions()[0]}};</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;        internal::IndexMapper&lt;Index, InputDims, 1, Layout&gt; indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160; </div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;        <span class="keyword">typedef</span> EigenConvolutionKernel&lt;InputEvaluator, CoeffReturnType, Scalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, InputDims,</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;                                       <span class="keyword">typename</span> KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV1D&gt;</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;            ConvKernel;</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160; </div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;        m_device.template binary_kernel_launcher&lt;CoeffReturnType, ConvKernel&gt;(</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;            m_inputImpl, m_kernel, data, cl::sycl::nd_range&lt;2&gt;(global_range, local_range), local_memory_size,</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;            indexMapper, kernel_size, cl::sycl::range&lt;2&gt;(input_dim[0], input_dim[1]));</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;      }</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160; </div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;      <span class="keywordflow">case</span> 2: {</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        <span class="keyword">auto</span> kernel_index = std::array&lt;size_t, 2&gt;{<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>) ? 0 : 1,</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;                                                  <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>) ? 1 : 0};</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;        <span class="keyword">auto</span> kernel_size = cl::sycl::range&lt;2&gt;{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]]};</div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numX = dimensions()[m_indices[kernel_index[0]]];</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numY = dimensions()[m_indices[kernel_index[1]]];</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numP = dimensions().TotalSize() / (numX * numY);</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        <span class="keyword">auto</span> input_dim = std::array&lt;size_t, 3&gt;{numX, numY, numP};</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160; </div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;        <span class="keyword">auto</span> global_range = cl::sycl::range&lt;3&gt;{};</div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        <span class="keyword">auto</span> local_range = cl::sycl::range&lt;3&gt;{};</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160; </div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        m_device.parallel_for_setup(input_dim, global_range, local_range);</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160; </div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_memory_size =</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;            (local_range[0] + kernel_size[0] - 1) * (local_range[1] + kernel_size[1] - 1) * local_range[2];</div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;        gpu_assert(<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">long</span><span class="keyword">&gt;</span>(local_memory_size) &lt;= m_device.sharedMemPerBlock());</div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;        <span class="keyword">const</span> array&lt;Index, 2&gt; indices{{m_indices[kernel_index[0]], m_indices[kernel_index[1]]}};</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        <span class="keyword">const</span> array&lt;Index, 2&gt; kernel_dims{</div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;            {m_kernelImpl.dimensions()[kernel_index[0]], m_kernelImpl.dimensions()[kernel_index[1]]}};</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        internal::IndexMapper&lt;Index, InputDims, 2, Layout&gt; indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;        <span class="keyword">typedef</span> EigenConvolutionKernel&lt;InputEvaluator, CoeffReturnType, Scalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, InputDims,</div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;                                       <span class="keyword">typename</span> KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV2D&gt;</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;            ConvKernel;</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;        m_device.template binary_kernel_launcher&lt;CoeffReturnType, ConvKernel&gt;(</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;            m_inputImpl, m_kernel, data, cl::sycl::nd_range&lt;3&gt;(global_range, local_range), local_memory_size,</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;            indexMapper, kernel_size, cl::sycl::range&lt;3&gt;{input_dim[0], input_dim[1], input_dim[2]});</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;        <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;      }</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160; </div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;      <span class="keywordflow">case</span> 3: {</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        <span class="keyword">auto</span> kernel_index = std::array&lt;size_t, 3&gt;{<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>) ? 0 : 2,</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;                                                  <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>) ? 1 : 1,</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;                                                  <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>) ? 2 : 0};</div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160; </div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;        <span class="keyword">auto</span> kernel_size = cl::sycl::range&lt;3&gt;{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]],</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;                                              (size_t)m_kernelImpl.dimensions()[kernel_index[2]]};</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160; </div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numX = dimensions()[m_indices[kernel_index[0]]];</div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numY = dimensions()[m_indices[kernel_index[1]]];</div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numZ = dimensions()[m_indices[kernel_index[2]]];</div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;        <span class="keyword">auto</span> input_dim = std::array&lt;size_t, 3&gt;{numX, numY, numZ};</div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> numP = dimensions().TotalSize() / (numX * numY * numZ);</div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160; </div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;        <span class="keyword">const</span> array&lt;Index, 3&gt; indices{</div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;            {m_indices[kernel_index[0]], m_indices[kernel_index[1]], m_indices[kernel_index[2]]}};</div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;        <span class="keyword">const</span> array&lt;Index, 3&gt; kernel_dims{{m_kernelImpl.dimensions()[kernel_index[0]],</div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;                                           m_kernelImpl.dimensions()[kernel_index[1]],</div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;                                           m_kernelImpl.dimensions()[kernel_index[2]]}};</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160; </div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        internal::IndexMapper&lt;Index, InputDims, 3, Layout&gt; indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160; </div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        <span class="keyword">auto</span> global_range = cl::sycl::range&lt;3&gt;{};</div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;        <span class="keyword">auto</span> local_range = cl::sycl::range&lt;3&gt;{};</div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160; </div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;        m_device.parallel_for_setup(input_dim, global_range, local_range);</div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;        <span class="keyword">auto</span> local_memory_range = (local_range + kernel_size - 1);</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> local_memory_size = local_memory_range[0] * local_memory_range[1] * local_memory_range[2];</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160; </div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;        gpu_assert(<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">long</span><span class="keyword">&gt;</span>(local_memory_size) &lt;= m_device.sharedMemPerBlock());</div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        <span class="keyword">typedef</span> EigenConvolutionKernel&lt;InputEvaluator, CoeffReturnType, Scalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, InputDims,</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;                                       <span class="keyword">typename</span> KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV3D&gt;</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;            ConvKernel;</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;        m_device.template binary_kernel_launcher&lt;CoeffReturnType, ConvKernel&gt;(</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;            m_inputImpl, m_kernel, data, cl::sycl::nd_range&lt;3&gt;(global_range, local_range), local_memory_size,</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;            indexMapper, kernel_size, cl::sycl::range&lt;3&gt;(input_dim[0], input_dim[1], input_dim[2]), numP);</div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;        <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;      }</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160; </div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;      <span class="keywordflow">default</span>: {</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;        EIGEN_STATIC_ASSERT((NumKernelDims &gt;= 1 &amp;&amp; NumKernelDims &lt;= 3),</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;                            THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;      }</div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    }</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;  }</div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160; </div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;    eigen_assert(m_buf != NULL);</div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;    eigen_assert(index &lt; m_dimensions.TotalSize());</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;    <span class="keywordflow">return</span> m_buf[index];</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;  }</div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160; </div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">int</span> LoadMode&gt;</div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(<span class="keyword">const</span> Index index)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    eigen_assert(m_buf != NULL);</div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    eigen_assert(index &lt; m_dimensions.TotalSize());</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    <span class="keywordflow">return</span> internal::ploadt&lt;PacketReturnType, LoadMode&gt;(m_buf + index);</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;  }</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160; </div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(<span class="keywordtype">bool</span> vectorized)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    <span class="comment">// TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost</span></div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    <span class="comment">// model.</span></div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">double</span> kernel_size = m_kernelImpl.dimensions().TotalSize();</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;    <span class="comment">// We ignore the use of fused multiply-add.</span></div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">double</span> convolve_compute_cost = TensorOpCost::AddCost&lt;Scalar&gt;() + TensorOpCost::MulCost&lt;Scalar&gt;();</div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">double</span> firstIndex_compute_cost =</div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;        NumDims *</div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;        (2 * TensorOpCost::AddCost&lt;Index&gt;() + 2 * TensorOpCost::MulCost&lt;Index&gt;() + TensorOpCost::DivCost&lt;Index&gt;());</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;    <span class="keywordflow">return</span> TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +</div>
<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;           kernel_size * (m_inputImpl.costPerCoeff(vectorized) + m_kernelImpl.costPerCoeff(vectorized) +</div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;                          TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize));</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;  }</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;  <span class="comment">// binding placeholder accessors to a command group handler for SYCL</span></div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keywordtype">void</span> bind(cl::sycl::handler &amp;cgh)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;    m_kernelImpl.bind(cgh);</div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    m_inputImpl.bind(cgh);</div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;    m_buf.bind(cgh);</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    m_kernel.bind(cgh);</div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;  }</div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160; </div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;  <span class="comment">// No assignment (copies are needed by the kernels)</span></div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;  TensorEvaluator &amp;operator=(<span class="keyword">const</span> TensorEvaluator &amp;);</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;  TensorEvaluator&lt;InputArgType, Eigen::SyclDevice&gt; m_inputImpl;</div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;  KernelArgType m_kernelArg;</div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;  TensorEvaluator&lt;KernelArgType, Eigen::SyclDevice&gt; m_kernelImpl;</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;  Indices m_indices;</div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;  Dimensions m_dimensions;</div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;  EvaluatorPointerType m_buf;</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;  <span class="keyword">typename</span> KernelStorage::Type m_kernel;</div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;  <span class="keywordtype">bool</span> m_local_kernel;</div>
<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;  <span class="keyword">const</span> Eigen::SyclDevice EIGEN_DEVICE_REF m_device;</div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;};  <span class="comment">// namespace Eigen</span></div>
<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160; </div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;}  <span class="comment">// end namespace Eigen</span></div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160; </div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">// EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H</span></div>
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